Journal of Computer Applications ›› 2021, Vol. 41 ›› Issue (10): 2928-2936.DOI: 10.11772/j.issn.1001-9081.2020121917

Special Issue: 先进计算

• Advanced computing • Previous Articles     Next Articles

Task allocation strategy in unmanned aerial vehicle-assisted mobile edge computing

WANG Daiwei, XU Gaochao, LI Long   

  1. College of Computer Science and Technology, Jilin University, Changchun Jilin 130012, China
  • Received:2020-12-09 Revised:2021-03-16 Online:2021-10-10 Published:2021-07-14
  • Supported by:
    This work is partially supported by the National Key Research and Development Program of China (2017YFA0604500), the Jilin Province Science and Technology Development Program (20190201180JC, 20200401076GX), the "13th Five-Year Plan" Science and Technology Research Program of Education Department of Jilin Province (JJKH20191310KJ).


王岱巍, 徐高潮, 李龙   

  1. 吉林大学 计算机科学与技术学院, 长春 130012
  • 通讯作者: 徐高潮
  • 作者简介:王岱巍(1994-),男,内蒙古呼伦贝尔人,硕士研究生,主要研究方向:移动边缘计算、无人机通信;徐高潮(1966-),男,湖北广水人,教授,博士,主要研究方向:分布式系统、云计算、物联网、网络安全;李龙(1994-),男,陕西宝鸡人,硕士研究生,主要研究方向:移动边缘计算、分布式计算、无线电能传输。
  • 基金资助:

Abstract: In the scenario of using Unmanned Aerial Vehicle (UAV) as the data collector for computation offloading to provide Mobile Edge Computing (MEC) services to User Equipment (UE), a wireless communication strategy to achieve efficient UE coverage through UAV was designed. Firstly, under the condition of a given UE distribution, for the UAV flight trajectory and communication strategy, an optimization method of Successive Convex Approximation (SCA) was used to obtain an approximate optimal solution that was able to minimize the global energy. In addition, for scenarios with large-scale distribution of UEs or a large number of tasks, an adaptive clustering algorithm was proposed to divide the UEs on the ground into as few clusters as possible, and to ensure the offloading data of all UEs in each cluster was able to be collected in one flight. Finally, the computation offloading data collection tasks of the UEs in each cluster were allocated to one flight, so that the goal of reducing the number of dispatches required for a single UAV or the UAV number of dispatches required for multiple UAVs to complete the task was achieved. The simulation results show that the proposed method can generate fewer clusters than the K-Means algorithm and converge quickly, and is suitable for UAV-assisted computation offloading scenarios with widely distributed UEs.

Key words: Unmanned Aerial Vehicle (UAV) communication, Mobile Edge Computing (MEC), convex optimization, Successive Convex Approximation (SCA), adaptive clustering

摘要: 在使用无人机(UAV)作为计算卸载的数据收集器对用户设备(UE)提供移动边缘计算(MEC)服务的场景下,设计了一种通过UAV实现高效的UE覆盖的无线通信策略。首先,在给定UE分布的条件下,对于UAV的飞行轨迹和通信策略,使用了连续凸逼近(SCA)的优化方法来得出一种可以使全局能量最小化的近似最优解;此外,对于UE大范围分布或任务量较大的场景,提出了一种自适应聚类算法,以将地面的UE划分成尽量少的聚类,并保证每个聚类中全部UE的卸载数据都可以在一次飞行中全部完成收集;最后,将每个聚类中UE的计算卸载数据收集任务分配给一次飞行,从而达到减少单个UAV完成任务所需的派遣次数或多UAV执行任务所需的UAV派遣数量的目的。仿真结果表明,所提方法可以生成相比K-Means算法更少的聚类数量且能快速收敛,适用于UE大范围分布下UAV辅助的计算卸载场景。

关键词: 无人机通信, 移动边缘计算, 凸优化, 连续凸逼近, 自适应聚类

CLC Number: